Reinforcement Learning in Process Industries:Review and Perspective

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

Strategies for Improving the Effectiveness of Professional Practice for Full-Time Professional Master Degree Postgraduate in Mineral Processing Engineering

In order to gain practical experience and hands-on skills,full-time professional master degree postgraduate in mineral processing engineering should engage in professional practices.Nonetheless,a series of problems,including insufficient time for practice,low management level,inadequate implementation of the double-supervisor system,and poor results of professional practice,has reduced the effectiveness of professional practice.In view of the aforementioned problems and the characteristics of the discipline,this paper proposes several strategies for improving the effectiveness of professional practice for postgraduates in mineral processing engineering.

Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.

Review on the study of metallurgical process engineering

After nearly one hundred years of research, metallurgy(metallurgical science and engineering) has gradually become a system with three levels of knowledge:(1) micro metallurgy at the atomic/molecular scale,(2) process metallurgy at the procedure/device, and(3) macrodynamic metallurgy at the full process/process group. Macro-dynamic metallurgy development must eliminate the concept of an "isolated system" and establish concepts of "flow," "process network," and "operating program" to study the "structure–function–efficiency" in the macrodynamic operation of metallurgical manufacturing processes. It means considering "flow" as the ontology and observing dynamic change by"flow" to solve the green and intelligent potential of metallurgical enterprises. Metallurgical process engineering is integrated metallurgy, toplevel designed metallurgy, macro-dynamic operated metallurgy, and engineering science level metallurgy. Metallurgical process engineering is a cross-level, comprehensive, and integrated study of the macro-dynamic operation of manufacturing processes. Metallurgical process engineering studies the physical nature and constitutive characteristics of the dynamic operation of steel manufacturing process, as well as the analysis-optimization of the set of procedure functions, coordination-optimization of the set of procedures' relations, and reconstruction-optimization of the set of procedures in the manufacturing process. The study establishes rules for the macro operation of the manufacturing process, as well as dynamic and precise objectives of engineering design and production operation.

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.

Intermolecular and Surface Interactions in Engineering Processes

Interactions involving chemical reagents,solid particles,gas bubbles,liquid droplets,and solid surfaces in complex fluids play a vital role in many engineering processes,such as froth flotation,emulsion and foam formation,adsorption,and fouling and anti-fouling phenomena.These interactions at the molecular,nano-,and micro scale significantly influence and determine the macroscopic performance and efficiency of related engineering processes.Understanding the intermolecular and surface interactions in engineering processes is of both fundamental and practical importance,which not only improves production technologies,but also provides valuable insights into the development of new materials.In this review,the typical intermolecular and surface interactions involved in various engineering processes,including Derjaguin–Landau–Verwey–Overbeek(DLVO)interactions(i.e.,van der Waals and electrical doublelayer interactions)and non-DLVO interactions,such as steric and hydrophobic interactions,are first introduced.Nanomechanical techniques such as atomic force microscopy and surface forces apparatus for quantifying the interaction forces of molecules and surfaces in complex fluids are briefly introduced.Our recent progress on characterizing the intermolecular and surface interactions in several engineering systems are reviewed,including mineral flotation,petroleum engineering,wastewater treatment,and energy storage materials.The correlation of these fundamental interaction mechanisms with practical applications in resolving engineering challenges and the perspectives of the research field have also been discussed.

Process engineering in electrochemical energy devices innovation

This review focuses on the application of process engineering in electrochemical energy conversion and storage devices innovation. For polymer electrolyte based devices, it highlights that a strategic simple switch from proton exchange membranes(PEMs) to hydroxide exchange membranes(HEMs) may lead to a new-generation of affordable electrochemical energy devices including fuel cells, electrolyzers, and solar hydrogen generators. For lithium-ion batteries, a series of advancements in design and chemistry are required for electric vehicle and energy storage applications. Manufacturing process development and optimization of the LiF eP O_4/C cathode materials and several emerging novel anode materials are also discussed using the authors' work as examples.Design and manufacturing process of lithium-ion battery electrodes are introduced in detail, and modeling and optimization of large-scale lithium-ion batteries are also presented. Electrochemical energy materials and device innovations can be further prompted by better understanding of the fundamental transport phenomena involved in unit operations.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Evolution process of rock mass engineering system using systems science

The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.

Analysis and Related Suggestions on the Whole Process Engineering Consulting Service Mode at Home and Abroad

This paper first introduces the basic connotation of China’s whole-process engineering consulting.Immediately,analyze the organization model,service procurement model and charging standards of foreign whole-process engineering consulting(international terminology full-life cycle engineering consultant).Second,discuss the government’s role in the development of engineering consulting from two aspects:service management and market access.Finally,combined with the above analysis,the specific problems faced in the implementation process of the whole process engineering consulting are compared.Provide relevant suggestions on how companies and individuals respond to industry development trends.

Mathematical modeling and analysis of thermodynamic processes in a twin-rotor piston engine

In order to study the major performance indicators of the twin-rotor piston engine(TRPE), Matlab/simulink was used to simulate the mathematical models of its thermodynamic processes. With consideration of the characteristics of the working processes in the TRPE, corresponding differential equations were established and then simplified by period features of the TRPE. Finally, the major boundary conditions were figured out. The changing trends of mass, pressure and temperature of working fuel in the working chamber during a complete engine cycle were presented. The simulation results are consistent with the trends of an actual working cycle in the TRPE, which indicates that the method of simulation is feasible. As the pressure in the working chamber is calculated, all the performance parameters of the TRPE can be obtained. The major performance indicators, such as the indicated mean effective pressure, power to weight ratio and the volume power, are also acquired. Compared with three different types of conventional engines, the TRPE has a bigger utilization ratio of cylinder volume, a higher power to weight ratio and a more compact structure. This indicates that TRPE is superior to conventional engines.

Enterprise Process Modeling Methodology and Environment Supporting Project Management

Development of the global economy makes modem enterprises confront challenges of efficient managements for large projects and complex processes. Projects are typically managed in rather special manners. On the contrary, there exist many methodologies for product process management to achieve consistency and continuance. However, processes often lack flexibility offered by projects. This paper dis~ the relationship of conceptual characteristics between process and project, gives low-level details to tackle the difference between them, and proposes an enterprise process modeling method for project management. An integrated environment is designed to support the method from which both project management and process management can receive benefits and conform to the limitations.

A Novel Mechanism for TRF of Plant-wide Material Flows in Process Industry MES

This paper is standing on the recent viewpoint originated from relevant industrial practices that well or-ganized tracing, representing and feedback(TRF) mechanism of material-flow information is crucial for system utility and usability of manufacturing execution systems(MES), essentially, for activities on the side of multi-level decision making and optimization mainly in the planning and scheduling. In this paper, we investigate a key issue emphasized on a route of multi-level information evolution on the side of large-scale feedback, where material-flow states could evolve from the measuring data(local states) to networked event-type information cells(global states) and consequently to the key performance indicators(KPI) type information(gross states). Importantly, with adapta-bilities to frequent structural dynamics residing in running material flows, this evolving route should be modeled as a suit of sophisticated mechanism for large-scale dynamic states tracking and representing so as to upgrade accu-racy and usability of the feedback information in MES. To clarify inherent complexities of this evolving route, the investigated issue is demonstrated from extended process systems engineering(PSE) point of view, and the TRF principles of the multi-level feedback information(states) are highlighted under the multi-scale methodology. As the main contribution, a novel mechanism called TRF modeling mechanism is introduced.

The application of business process re-engineering in Chinese enterprises

After joining in WTO,Chinese enterprises face more intense competitive environments. The problems, such as many foreign capital enterprises swarming into China, increasing market competitive ability, keeping on national and international market, have become more serious to enterprise’s decision maker. At the same time, the information technology is developing quickly, then business process re engineering will become valid path for enterprise increasing market competitive ability. As a case study, this paper applies Business Process Re engineering(BPR) approach to analyze a Chinese company.

Discussion on the Teaching of“Metallic Mineral Processing”for Mineral Processing Engineering

Social economic growth and the increasing demand for mineral resources have promoted the development of metallic mineral processing technology.Therefore,in order to satisfy the demands for development in mining,cultivating comprehensive mineral processing engineering professionals with strong innovative practical skills has become the top priority in current education.We have established a new course,“Metallic Mineral Processing,”for students majoring in mineral processing engineering in universities,with coal and other sources of energy as the main focus.This paper analyzes the purpose and significance of setting up this course and the exploration of the reform of the teaching mode,with the aim of improving the teaching quality and ensuring the cultivation of mineral processing engineering undergraduates.

PRODUCT MODELING AND PROCESS PLANNING WITHIN CONCURRENT ENGINEERING ENVIRONMENT

A prodouct modeling and a process planning that are two essential basses of realizing concurrent engineering are investigated , a logical modeling technique , grammar representation scheme of technology knowledge and architecture of expert system for process planning within con- current engineering environment are proposed. They have been utilized in a real reaserch project.

Online Nonstop Task Management for Storm-Based Distributed Stream Processing Engines

Most distributed stream processing engines(DSPEs)do not support online task management and cannot adapt to time-varying data flows.Recently,some studies have proposed online task deployment algorithms to solve this problem.However,these approaches do not guarantee the Quality of Service(QoS)when the task deployment changes at runtime,because the task migrations caused by the change of task deployments will impose an exorbitant cost.We study one of the most popular DSPEs,Apache Storm,and find out that when a task needs to be migrated,Storm has to stop the resource(implemented as a process of Worker in Storm)where the task is deployed.This will lead to the stop and restart of all tasks in the resource,resulting in the poor performance of task migrations.Aiming to solve this problem,in this pa-per,we propose N-Storm(Nonstop Storm),which is a task-resource decoupling DSPE.N-Storm allows tasks allocated to resources to be changed at runtime,which is implemented by a thread-level scheme for task migrations.Particularly,we add a local shared key/value store on each node to make resources aware of the changes in the allocation plan.Thus,each resource can manage its tasks at runtime.Based on N-Storm,we further propose Online Task Deployment(OTD).Differ-ing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migra-tions caused by a task re-deployment,OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of resources.We demonstrate that OTD can adapt to different kinds of applications including computation-and communication-intensive applications.The experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87%of the performance degradation time,compared with Apache Storm and other state-of-the-art approaches.In addition,OTD can increase the average CPU usage by 51%for computation-intensive applications and reduce network communication costs by 88%for communication-intensive ap-plications.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

Machine Learning in Chemical Engineering:Strengths,Weaknesses,Opportunities,and Threats

Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.

A systematic approach for synthesizing a low-temperature distillation system

In this paper, by combining a stochastic optimization method with a refrigeration shaft work targeting method,an approach for the synthesis of a heat integrated complex distillation system in a low-temperature process is presented. The synthesis problem is formulated as a mixed-integer nonlinear programming(MINLP) problem,which is solved by simulated annealing algorithm under a random procedure to explore the optimal operating parameters and the distillation sequence structure. The shaft work targeting method is used to evaluate the minimum energy cost of the corresponding separation system during the optimization without any need for a detailed design for the heat exchanger network(HEN) and the refrigeration system(RS). The method presented in the paper can dramatically reduce the scale and complexity of the problem. A case study of ethylene cold-end separation is used to illustrate the application of the approach. Compared with the original industrial scheme, the result is encouraging.